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Snow microstructure plays an important role in the remote sensing of snow water equivalent (SWE) for both passive and active microwave radars. The accuracy of microwave SWE retrieval algorithms is sensitive to (usually unknown) changes in microstructure. These algorithms could be improved with high-resolution estimates of microstructural properties by using an advanced instrument such as the Snow Micro Penetrometer (SMP), which measures penetration force at the millimeter scale and is sensitive to microstructure. The SMP can also take full micromechanical measurements at much greater speed and resolution and without observer bias than a traditional snow pit. Previous studies have shown that the snowpack stratigraphy and grain type can be accurately classified with one SMP measurement using basic statistics and classification trees (CTs). For this study, we used basic statistical measures of the penetration force and micromechanical estimates from an SMP inversion algorithm to significantly improve the classification accuracy of grain type and layer discrimination. We applied random forest (RF) techniques to classify three snow grain types (new snow, rounds, and facets) from SMP measurements collected in Switzerland and Grand Mesa, Colorado. RFs performed up to 8% better than single CTs, with overall misclassification errors between 17% and 40%. The coefficient of variation of the penetration force proved to be the most important variable, followed by variables that contain information about grain size like microscale strength and the number of ruptures. [ABSTRACT FROM AUTHOR]

Reproducible science requires a shift in thinking and application for how data, code and analysis are shared. Now, scientists must act more like software engineers to design models and perform analysis that use principles and techniques pioneered by software developers. Creating reproducible models that are easy to use and understand is in the best interest for the snow and hydrology community, enabling studies by other researchers and facilitating technology transfer to operational applications. Here, we present the Automated Water Supply Model (AWSM) that streamlines and standardizes the workflow of a physically based snow model to create fully reproducible model simulations that can be utilized by researchers and operational water resource managers. AWSM orchestrates four core components that historically required significant, ad-hoc modeler interaction to load the input data, spatially interpolate to the modeling domain, run the models and process the outputs. Because AWSM was developed using principles and techniques from software engineering, users can quickly perform reproducible simulations on any operating system, from a laptop to the cloud. The three fully reproducible example case studies showcase the simplicity and flexibility of using AWSM to perform simulations from small research catchments to simulations that aid in real time water management decisions. • A reproducible scientific framework for a physically based snow model. • The Python framework can be used for research or operational applications. • Follows software engineering best practices for code versioning and packaging. • Three reproducible example case studs providing readers insight to modeling system. [ABSTRACT FROM AUTHOR]